Video OCR: indexing digital news libraries by recognition of superimposed captions

The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multi-frame integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.     

Highly accurate recognition of printed Korean characters through an improved two-stage classification method

This paper presents a recognition system which obtains a recognition rate higher than 99% for the printed Korean characters of multifont and multisize. We recognize a given input by first identifying the character type of the input and then recognizing its constituent graphemes. In order to improve the performance we incorporated three new ideas in our system: the expansion of the subimage areas used by the grapheme classifiers, an algorithm to accurately segment the horizontal vowel’s subimage areas, and a validation process to evaluate the result of the type classifier. Through experiments we confirmed that our system performs well in a multi-font and multi-size environment and that those three ideas actually contributed to improve the performance significantly.     

基于双光源图像合成法的标牌字符识别方法研究

在电能表标牌OCR的识别过程中,标牌上的透明薄膜经常出现皱褶,使得待识别字符极易受到反射光遮挡,从而增加了OCR的识别难度和识别错误率。提出一种位置对称的双光源图像合成法,用该方法检测了多个贴有皱褶薄膜层的电能表标牌,经实例验证,相较于单光源的识别错误率大幅度降低,这有效解决了电能表标牌OCR中薄膜反光干扰问题。     

Pitch-based segmentation and recognition of dot-matrix text

Dot-matrix text recognition is a difficult problem, especially when characters are broken into several disconnected components. We present a dot-matrix text recognition system which uses the fact that dot-matrix fonts are fixed-pitch, in order to overcome the difficulty of the segmentation process. After finding the most likely pitch of the text, a decision is made as to whether the text is written in a fixed-pitch or proportional font. Fixed-pitch text is segmented using a pitch-based segmentation process that can successfully segment both touching and broken characters. We report performance results for the pitch estimation, fixed-pitch decision and segmentation, and recognition processes. Received October 18, 1999 / Revised April 21, 2000     

基于图理论聚类的彩色图像文本提取

本文提出了一种在彩色图像中进行文本区域的自动提取的方法。首先，应用色彩的统计模型，大大减小了图像的彩色空间的大小；其次，使用基于图理论进行彩色聚类。将图像分解成对应各类的多幅二值图；然后，在这些二值图的基础上进行连通分量分析，提取可能的文本区域，并对这些区域进行鉴别；最后，综合各二值图的提取结果，得到原始彩色图像中的文本区域。对于特定的应用，提取出的文本区域经过进一步的处理，可以输入字符识别(0CR）系统中进行识别。实验结果显示了本文提出的方法的有效性．     

基于Android的随身客户信息管理系统的设计与开发

介绍了基于Android的随身客户信息管理系统的体系结构、核心功能模块的实现,具体包括用户登陆及识别、手机终端拍照成像、名片识别、客户信息云同步、名片分享与交换、维系客户等功能,并在此基础上重点给出了部分功能的实现代码。     

基于OCR的纵向文字校对的研究与实现*

提出一种基于OCR识别的文字纵向校对的实现方法,在文字信息处理、图像显示和校对过程中提出了自己的算法和策略,对数组结构及其读写方法进行了较细致的分析。以汉字的纵向校对为例,实验表明该方法具有很快的计算速度,对一个有经验的校对人员而言,通过纵校和横校的结合可以使差错率控制在万分之三以内。     

An expert system for general symbol recognition

An expert system for analysis and recognition of general symbols is introduced. The system uses the structural pattern recognition technique for modeling symbols by a set of straight lines referred to as segments. The system rotates, scales and thins the symbol, then extracts the symbol strokes. Each stroke is transferred into segments (straight lines). The system is shown to be able to map similar styles of the symbol to the same representation. When the system had some stored models for each symbol (an average of 97 models/symbol), the rejection rate was 16.1% and the recognition rate was 83.9% of which 95% was recognized correctly. The system is tested by 5726 handwritten characters from the Center of Excellence for Document Analysis and Recognition (CEDAR) database. The system is capable of learning new symbols by simply adding their models to the system knowledge base.     

浅谈人工智能在融媒体业务生产中的应用发展

近年来,随着IT技术和互联网的发展,人工智能AI(Artificial Intelligence)技术在广电行业得到广泛应用。文章基于融媒体环境下人工智能技术的应用与发展进行了探讨,分析人工智能技术如何推动生产效率提高以及智能机器人书写稿件、智能人脸识别、智能语音语义识别、智能化的OCR识别、自动播出等方面的应用与发展。